Katerina V. Djambazova

IsoMobil: Resolving Molecular Ambiguity in Mass Spectrometry-based Spatial Omics Through Ion Mobility

IsoMobil reduces high-dimensional ion mobility mass spectrometry data by grouping isotopologues, improving interpretation at the molecular-species level.

Elucidating Molecular Features of White Matter Hyperintensities in Alzheimer’s Disease through Multimodal Imaging and SHAP Analysis

This study identifies region-specific lipid alterations in Alzheimer’s disease white matter hyperintensities using multimodal imaging and SHAP analysis.

IsoSpace: Chemistry-Informed Dimensionality Reduction and Automated Isotope Candidate Detection in Imaging Mass Spectrometry

IsoSpace provides a chemistry-informed dimensionality reduction method for imaging mass spectrometry that identifies isotopic patterns without prior assumptions.

Spatial Dependence and Heterogeneity in Molecular Imaging: Moran Quadrant Maps Enable Advanced Spatial-Statistical Analysis

The paper introduces Moran quadrant maps and workflows for spatial-statistical analysis of molecular imaging data, enabling scalable exploration of spatial dependence and heterogeneity.

Cellular and Spatial Drivers of Unresolved Injury and Functional Decline in the Human Kidney

The paper presents a multi-modal atlas of human kidney disease, identifying cellular and spatial mechanisms of injury resolution or failure and linking them to clinical outcomes.

Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

This paper introduces an automated workflow using SHAP to identify biomarker candidates in imaging mass spectrometry data by ranking molecular species based on their predictive importance for biological class recognition.